The goal of thes paper is to analyze predictability of future asset returns in the context of model uncertainty. Using data for the euro area, the US and the U.K., we show that one can improve the forecasts of stock returns using a Bayesian Model Averaging (BMA) approach, and there is a large amount of model uncertainty. The empirical evidence for the euro area suggests that several macroeconomic, financial and macro-financial variables are consistently among the most prominent determinants of risk premium. As for the U.S, only a few number of predictors play an important role. In the case of the UK, future stock returns are better forecasted by financial variables. These results are corroborated for both the M-open and the M-closed perspectives and in the context of "in-sample" and "out-of-sample" forescating. Finally, we highlight that the predictive ability of the BMA framework is stronger at longer periods, and clearly outperforms the constant expected returns and the autoregressive benchmark models.; Fundação para a Ciência e a Tecnologia (FCT)

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend–price and smoothed earnings–price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference.; Economics

We develop an approximate solution method for the optimal consumption and portfolio choice problem of an infinitely long-lived investor with Epstein–Zin utility who faces a set of asset returns described by a vector autoregression in returns and state variables. Empirical estimates in long-run annual and post-war quarterly U.S. data suggest that the predictability of stock returns greatly increases the optimal demand for stocks. The role of nominal bonds in long-term portfolios depends on the importance of real interest rate risk relative to other sources of risk. Long-term inflation-indexed bonds greatly increase the utility of conservative investors.; Economics

(cont.) Slow information diffusion can cause return momentum. Institutions are thought to be more informed than individuals, and should eliminate return predictability. However, higher institutional ownership is associated with more momentum. Therefore, institutions either herd on returns or can have information before individuals. I find evidence of the latter. However, the effects are economically small, suggesting that aggregate data obscures differences between institutions. I divide institutions by trading aggressiveness. Aggressive institutions are more responsive to recent returns, and a strategy mimicking their trades generates even better performance. This confirms that some investors are more informed than others, but do not eliminate return predictability.; This thesis consists of three chapters, each about a separate aspect of how investors respond to information in equity markets. The first chapter concerns news and stock returns. Using a comprehensive database of headlines about individual companies, I examine monthly returns following public news. I compare them to stocks with similar returns, but no identifiable public news. There is a difference between the two sets. I find strong drift after bad news. Investors seem to react slowly to this information. I also find reversal after extreme price movements unaccompanied by public news. The separate patterns appear even after adjustments for risk exposure and other effects. They are...

This thesis consists of three essays on asset pricing. Chapter 1 presents an equilibrium model to study the convergence trading of large hedge funds in segmented markets. The model provides an alternative explanation for the anomaly of a price gap between two fundamentally identical securities. Strategic arbitrageurs, taking into account their price impact, do not close the price gap. This gap makes investors who trade with the strategic arbitrageur less willing to invest in risky assets ex ante. Thus, the gap brings an additional source of risk, anticipation risk. The model predicts that the future price gap is wide when the current trading volume is high. Daily data for Royal Dutch and Shell provides evidence in support of the model. Chapter 2 (coauthored with Joon Chae and Andrew W. Lo) is about stock price behavior. We perform various statistical analyses on stock market returns as Fama (1965) did, using CRSP index returns from 1926-2005. We investigate stock return distributions and report return characteristics. First, stock returns do not follow a normal distribution, though many studies assume this.; (cont.) Second, autocorrelations of stock returns are not zero (as verified by many predictability studies). In addition, the level of autocorrelations varies widely across time. Third...

This thesis consists of three chapters exploring predictability of stock returns. In the first chapter, I suggest a new approach to analysis of stock return predictability. Instead of relying on predictive regressions, I employ a state space framework. Acknowledging that expected returns and expected dividends are unobservable, I use the Kalman filter technique to extract them from the observed history of realized dividends and returns. The suggested approach explicitly accounts for the possibility that dividend growth can be predictable. Moreover, it appears to be more robust to structural breaks in the long-run relation between prices and dividends than the conventional OLS regression. I show that for aggregate stock returns the constructed forecasting variable provides statistically and economically significant predictions both in and out of sample. The likelihood ratio test based on a simulated finite sample distribution of the test statistic rejects the hypothesis of constant expected returns at the 1% level. In the second chapter, I analyze predictability of returns on value and growth portfolios and examine time variation of the value premium. As a major tool, I use the filtering technique developed in the first chapter. I construct novel predictors for returns and dividend growth on the value and growth portfolios and find that returns on growth stocks are much more predictable than returns on value stocks. Applying the appropriately modified state space approach to the HML portfolio...

Out-of-sample tests of forecast performance depend on how a given data set is split into estimation and evaluation periods, yet no guidance exists on how to choose the split point. Empirical forecast evaluation results can therefore be di cult to interpret, particularly when several values of the split point might have been considered. When the sample split is viewed as a choice variable, rather than being fixed ex ante, we show that very large size distortions can occur for conventional tests of predictive accuracy. Spurious rejections are most likely to occur with a short evaluation sample, while conversely the power of forecast evaluation tests is strongest with long out-of-sample periods. To deal with size distortions, we propose a test statistic that is robust to the effect of considering multiple sample split points. Empirical applications to predictability of stock returns and inflation demonstrate that out-of-sample forecast evaluation results can critically depend on how the sample split is determined.

This paper brings together two separate and important topics in finance: the predictability of
aggregated stock returns and the intertemporal asset pricing models. We present empirical
evidence about the predictability of stock returns with a sample of OECD economies and
investigate whether such evidence is consistent with a simple general equilibrium model. Our
framework allow us to formalize the extensively documented empirical relationship between asset
returns and real activity. The principal parameters in this relationship are the relative risk aversion
and the elasticity of intertemporal substitution for the first moment of the returns and only the
elasticity of substitution for the second moments. Except for the United States annual case, the
puzzle of volatility remains in our model.

A number of recent papers have analyzed the degree of predictability of stock markets. In this paper, we firstly study whether this predictability is really exploitable and secondly, if the economic significance of predictability is higher or lower in the emerging stock markets than in the developed ones. We use a variety of linear and nonlinear – Artificial Neural Networks – models and perform a computationally demanding forecasting experiment to assess the predictability of returns. Since we are interested in comparing the predictability in economic terms we also propose a modification in the nets’ loss function for market trading purposes. In addition, we consider both explicit and implicit trading costs for emerging and developed stock markets. Our conclusions suggest that, in contrast to some previous studies, if we consider total trading costs both the emerging as well as the developed stock returns are clearly nonpredictable. Finally, we find that Artificial Neural Networks do not provide superior performance than the linear models.

This paper examines whether a general equilibrium asset pricing model can explain two
important empirical regularities of asset returns, extensively documented in the literature: (i)
returns can be predicted by a set of macro variables, and (ii) returns are very volatile. We
derive a closed-form solution for the equilibrium asset pricing model that relates asset returns
to output by using an approximate method proposed by Campbell (Am. Econ. Rev. 83 (1993)
487) and Restoy and Weil (W.P. NBER, No. 6611 (1998)). We obtain evidence on eight
OECD economies using both quarterly and annual observations. Equilibrium models seem to
fin fewer difficultie in explaining the volatility of returns than their predictability for general
output processes. In the case of the US, the observed predictability and volatility of asset
returns, for annual frequencies, are broadly compatible with the predictions of equilibrium
models for a reasonable

This paper analyzes whether web search queries predict stock market activity in a
sample of the largest European stocks. We provide evidence that i) an increase in web
searches for stocks on Google engine is followed by a temporary increase in volatility
and volume and a drop in cumulative returns. ii) An increase for web search queries for
the market index leads to a decrease in the returns of the index as well as of the stock
index futures and an increase in implied volatility. iii) Attention interacts with
behavioral biases. The predictability of web searches for return and liquidity is
enhanced when firm prices and market prices hit a 52-week high and diminished when
the market hits a 52-week low. iv) Investors tend to process more market information
than firm specific information in investment decisions, confirming limited attention
theory.

This paper employs Bayesian dynamic linear forecasting techniques to investigate the factors driving the predictability of Australian stock market. The unanticipated components of a set of economic and financial variables are chosen as the proxies for the economic risk factors that influence the industrial stock returns. The prior information is incorporated with the predictor variables and updated at each month during the sample period. The final test result reveals that the unanticipated components of term structure and short-term interest rate are the most significant variables to be priced in industry returns. The aggregate dividend-yield variable has influence on some of the industries. The industrial return's predictability is well explained by the time-varying risk premium of economic factors. The comparison between multivariate analysis and univariate analysis strongly indicates that the correlations within the industries are critical in the investigation of the predictability of returns.; http://www.elsevier.com/wps/find/journaldescription.cws_home/523619/description#description; Juan Yao, Jiti Gao and Lakshman Alles

Using the theoretical framework of Lettau and Ludvigson (2001), we perform an
empirical investigation on how widespread is the predictability of cay { a modi ed
consumption-wealth ratio { once we consider a set of important countries from a
global perspective. We chose to work with the set of G7 countries, which represent
more than 64% of net global wealth and 46% of global GDP at market exchange
rates. We evaluate the forecasting performance of cay using a panel-data approach,
since applying cointegration and other time-series techniques is now standard prac-
tice in the panel-data literature. Hence, we generalize Lettau and Ludvigson's tests
for a panel of important countries.
We employ macroeconomic and nancial quarterly data for the group of G7
countries, forming an unbalanced panel. For most countries, data is available from
the early 1990s until 2014Q1, but for the U.S. economy it is available from 1981Q1
through 2014Q1.
Results of an exhaustive empirical investigation are overwhelmingly in favor of
the predictive power of cay in forecasting future stock returns and excess returns.

This paper analyzes the in-, and out-of sample, predictability of the stock market returns from Eurozone’s banking sectors, arising from bank-specific ratios and macroeconomic variables, using panel estimation techniques. In order to do that, I set an unbalanced panel of 116 banks returns, from April, 1991, to March, 2013, to constitute equal-weighted country-sorted portfolios representative of the Austrian, Belgian, Finish, French, German, Greek, Irish, Italian, Portuguese and Spanish banking sectors. I find that both earnings per share (EPS) and the ratio of total loans to total assets have in-sample predictive power over the portfolios’ monthly returns whereas, regarding the cross-section of annual returns, only EPS retain significant explanatory power. Nevertheless, the sign associated with the impact of EPS is contrarian to the results of past literature. When looking at inter-yearly horizon returns, I document in-sample predictive power arising from the ratios of provisions to net interest income, and non-interest income to net income. Regarding the out-of-sample performance of the proposed models, I find that these would only beat the portfolios’ historical mean on the month following the disclosure of year-end financial statements. Still...

This paper presents empirical evidence of short and long-run predictability in stock returns for European transition economies. We employ variance ratios with a bootstrap methodology to test for short-run
predictability, which is present in most countries. We also estimate Hurst exponents to test for long-range dependence, and find evidence of such. Furthermore, we find evidence of strong time-varying long-range
dependence in these economies stock returns, which is in linewith evidence of multifractality of equity returns.

Motivated by the literature on investment flows and optimal trading, we
examine intraday predictability in the cross-section of stock returns. We find
a striking pattern of return continuation at half-hour intervals that are exact
multiples of a trading day, and this effect lasts for at least 40 trading days.
Volume, order imbalance, volatility, and bid-ask spreads exhibit similar
patterns, but do not explain the return patterns. We also show that short-term
return reversal is driven by temporary liquidity imbalances lasting less than
an hour and bid-ask bounce. Timing trades can reduce execution costs by the
equivalent of the effective spread.

We propose that predictability is a prerequisite for profitability on
financial markets. We look at ways to measure predictability of price changes
using information theoretic approach and employ them on all historical data
available for NYSE 100 stocks. This allows us to determine whether frequency of
sampling price changes affects the predictability of those. We also relations
between price changes predictability and the deviation of the price formation
processes from iid as well as the stock's sector. We also briefly comment on
the complicated relationship between predictability of price changes and the
profitability of algorithmic trading.; Comment: 8 pages, 16 figures, submitted for possible publication to
Computational Intelligence for Financial Engineering and Economics 2014
conference

In our previous studies we have investigated the structural complexity of
time series describing stock returns on New York's and Warsaw's stock
exchanges, by employing two estimators of Shannon's entropy rate based on
Lempel-Ziv and Context Tree Weighting algorithms, which were originally used
for data compression. Such structural complexity of the time series describing
logarithmic stock returns can be used as a measure of the inherent (model-free)
predictability of the underlying price formation processes, testing the
Efficient-Market Hypothesis in practice. We have also correlated the estimated
predictability with the profitability of standard trading algorithms, and found
that these do not use the structure inherent in the stock returns to any
significant degree. To find a way to use the structural complexity of the stock
returns for the purpose of predictions we propose the Maximum Entropy
Production Principle as applied to stock returns, and test it on the two
mentioned markets, inquiring into whether it is possible to enhance prediction
of stock returns based on the structural complexity of these and the mentioned
principle.; Comment: 14 pages, 5 figures

We document that the firm level hiring rate predicts stock returns in the cross-section of US publicly traded firms even after controlling for investment, size, book-to-market and momentum as well as other known predictors of stock returns. The predictability shows up in both Fama-MacBeth cross sectional regressions and in portfolio sorts and it is robust to the exclusion of micro cap firms from the sample. We propose a production-based asset pricing model with adjustment costs in labor and capital that replicates the main empirical findings well. Labor adjustment costs makes hiring decisions forward looking in nature and thus informative about the firms’ expectations about future cash-flows and risk-adjusted discount rates. The model implies that the investment rate and the hiring rate predicts stock returns because these variables proxy for the firm’s time-varying conditional beta.